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Tytuł artykułu

Forecasting Economic, Social and Environmental Growth in the Sanitary and Service Sector Based on Thailand’s Sustainable Development Policy

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The purpose of this study is to forecast the long run implementation of Thailand’s sustainable development policy in three main aspects, including economic, social and environmental aspect for the the sanitary and service sectors from 2016 until 2045. According to the national data for the years 2000-2015, based on the ARIMAX model, it has been found that Thai economy system is potentially changed and growing rapidly by 25.76%, the population has grown by 7.15%, and the Greenhouse gas emissions will gradually increase by 49.65%, in the year 2045. However, based on the analysis above, if Thailand fails to run the afore-mentioned policy properly, it will be difficult to successfully implement sustainable development, because the increased emission is moving in the same direction with economy and social aspect of Thailand.
Rocznik
Strony
205--210
Opis fizyczny
Bibliogr. 18 poz., tab.
Twórcy
  • Faculty of Economics, Chulalongkorn University, Wang Mai, Khet Pathum Wan, Bangkok, Thailand
Bibliografia
  • 1. Asian Development Bank (ADB). 2014. Environment, Climate Change, and Disaster Risk Management. Manila. Asian Development Bank.
  • 2. Azadeh A., Asadzadeh S., Saberi M., Nadimi V., Tajvidi A., Sheikalishahi M. 2011. A neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behawior analysis: the cases of Bahrain, Saudi Arabia, Syria, and UAE. Appl Energy, 88, 3850-9.
  • 3. Chienwattanasook Krisada, Sutthichaimethee Pruethsan. 2012. Trend of Thailand Jewelry Export to the USA Market. International Academy of Business and Economics, 12(3).
  • 4. Dong B., Coa C., Lee S.E. 2015. Applying suport vector machines to predict building energy consumption in tropical region. Energy Build, 37, 545-553.
  • 5. Hao J., Liu D., Li Z., Chen Z., Kong L. 2012. Power system load forecasting based on fuzzy clustering and gray target theory. Energy Proc, 16, 1852-9.
  • 6. Jovanovic RZ., Sretenovic´ AA., Zivkovic´ BD. 2015. Ensemble of various neural networks for prediction of heating energy consumption. Energy Build, 94, 189-99.
  • 7. Lee Y-S., Tong L-I. 2012. Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. Appl Energy, 94, 251-6.
  • 8. Office of the National Economic and Social Development Board. 2015. National Income of Thailand. Bangkok: NESDB.
  • 9. Osorio G., Matias J., Catalão J. 2015. Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information. Renew Energy, 75, 301-307.
  • 10. Sutthichaimethee P. 2017. VARIMAX Model to Forecast the emission of Carbon Dioxide from Energy Consumption in Rubber and Petroleum industries sectors in Thailand. Journal of Ecological Engineering, 18(3), 112-117.
  • 11. Sutthichaimethee P., Tanoamchard W. 2015. Carrying Capacity Model of Food Manufacturing Sectors for Sustainable Development from using Environmental and Natural Resources of Thailand. Journal of Ecological Engineering, 16(5), 1-8.
  • 12. Sutthichaimethee P., Ariyasajjakorn D. 2017. Forecasting Energy Consumption in Short-Term and Long-Term Period by using Arimax Model in the Construction and Materials Sector in Thailand. Journal of Ecological Engineering, 18(4), 52-59.
  • 13. Sutthichaimethee P., Sawangdee Y. 2016a. Model of Environmental Impact of Service Sectors to Promote Sustainable Development of Thailand. Ethics Sci Environ Polit, 16(1).
  • 14. Sutthichaimethee P., Sawangdee Y. 2016b. Indicator of Environmental Problems Priority Arising from the use of Environmental and Natural Resources in Machinery Sectors of Thailand. Environmental and Climate Technologies, 17(1), 18-29.
  • 15. Thailand Development Research Institute (TDRI). 2007. Prioritizing Environmental Problems with Environmental Costs. Final report prepared the Thailand Health Fund. Bangkok.
  • 16. Xie N-M., Yuan C-Q., Yang Y-J. 2015. Forecasting China’s energy demand and self sufficiency rate by grey forecasting model and Markov model. International Journal of Electrical Power and Energy Systems, 66, 1-8.
  • 17. Weijun Xu, Ren Gu, Youzhu Liu, Yongwu Dai. 2015. Forecasting energy consumption using a new GM–ARMA model based on HP filter: The case of Guangdong Province of China. Economic Modelling, 45, 127-135.
  • 18. Zhao H., Magoulès F. 2012. A review on the prediction of building energy consumption. Renewable Sustainable Energy Rev, 16, 3586-3592.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-83371fe2-4f49-4aee-bcbf-f846cec7331b
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